Prediction of Surface Oil Rates for Volatile Oil and Gas Condensate Reservoirs Using Artificial Intelligence Techniques

2021 ◽  
pp. 1-14
Author(s):  
Redha Al Dhaif ◽  
Ahmed Farid Ibrahim ◽  
Salaheldin Elkatatny
Keyword(s):  
Performance Test ◽  
Oil And Gas ◽  
Volatile Oil ◽  
Critical Flow ◽  
Well Test ◽  
Well Performance ◽  
Flow Conditions ◽  
Production Rates ◽  
Subcritical Flow ◽  
Well Production

Abstract Allocated well production rates are crucial to evaluate the well performance. Test separators and flow meters were replaced with choke formulas due to economic and technical issues special for high gas-oil ratio (GOR) reservoirs. This study implements Adaptive network-based fuzzy logic (ANFIS), and functional networks (FN) techniques to predict the oil rate through wellhead chokes. A set of data containing 1,200 wells was obtained from actual oil fields in the Middle East. The dataset included GOR, upstream and downstream pressure, choke size and actual oil and gas rates based on well test. GOR varied from 1,000 to 9,265 scf/stb, while oil rates ranged between 1,156 and 7,982 stb/d. Around 650 wells were flowing under critical flow conditions, while the rest were subcritical. Seventy percent of the data was used to train the AI models, while thirty percent of the data was used to test and validate these models. The developed AI models were then compared against the previous formulas. For subcritical flow conditions, rate prediction was correlated to both upstream and downstream pressures. While at critical flow conditions, changes in the downstream pressure did not affect the prediction of the production rates. For each AI method, two models were developed for subcritical flow and critical flow conditions. The average absolute percent error (AAPE) in the case of subcritical flow for ANFIS and FN were 0.88, and 1.01%, respectively. While in the case of critical flow, the AAPE values were 1.07, and 1.3% for ANFIS and FN models, respectively. All developed AI models outperform the published formulas, where the AAPE values for published formulas were higher than 34%. The results from this study will greatly assist petroleum engineers to predict the oil and gas rates based on available data from wellhead chokes in real-time with no need for additional operational costs or field intervention.

Machine Learning Applications to Predict Surface Oil Rates for High Gas Oil Ratio Reservoirs

2021 ◽  
pp. 1-19
Author(s):  
Ahmed Farid Ibrahim ◽  
Redha Al Dhaif ◽  
Salaheldin Elkatatny ◽  
Dhafer Al-Shehri
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Gas Production ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Support Vector ◽  
Critical Flow ◽  
Flow Conditions ◽  
Production Rates ◽  
Subcritical Flow

Abstract Well-performance investigation highly depends on the accurate estimation of its oil and gas flow rates. Testing separators and multiphase flow meters are associated with many technical and operational issues. Therefore, this study aims to implement the support vector machine (SVM), and random forests (RF) as machine learning (ML) methods to estimate the well production rate based on chokes parameters for high GOR reservoirs. Dataset of 1,131 data points includes GOR, upstream and downstream pressures (PU, and PD), choke size (D64), and actual data of oil and gas production rates. The data have GOR was up to 9,265 SCF/STB, the oil rate varied from 1,156 and 7,982 BPD. SVM and RF models were built to estimate the production rates. The ML models were trained using seventy percent of the dataset, while the models were tested and validated using thirty percent of the dataset. The dataset was classified to 622 wells that were flowing at critical flow compared to 509 wells that were flowing at subcritical conditions based on a PD/PU ratio of 0.55. Four machine learning models were developed using SVM and RF for subcritical flow and critical flow conditions. Different performance indicators were applied to assess the developed models. SVM and RF models revealed average absolute percent error (AAPE) of 1.3, and 0.7%, respectively in the case of subcritical flow conditions. For critical flow conditions, the AAPE was found to be 1.7% in the SVM model, and 0.8% in the RF model. The developed models showed a coefficient of determination (R2) higher than 0.93. All developed ML models perform better than empirical correlations. These results confirm the capabilities to predict the oil rates from the choke parameters in real-time without the requirement of instrument installation of wellsite intervention.

scholarly journals EPS Conceptual Design for GoM Deepwater Fields

2010 ◽  
Author(s):  
Michael Choi ◽  
Andrew Kilner ◽  
Hayden Marcollo ◽  
Tim Withall ◽  
Chris Carra ◽  
...  
Keyword(s):  
Conceptual Design ◽  
Oil And Gas ◽  
Cost Effective ◽  
Test System ◽  
Well Test ◽  
Well Performance ◽  
Compressed Natural Gas ◽  
Full Field ◽  
Field Development ◽  
Early Production

To avoid making billion dollar mistakes, operators with discoveries in deepwater (∼3,000m) Gulf of Mexico (GoM) need dependable well performance, reservoir response and fluid data to guide full-field development decisions. Recognizing this need, the DeepStar consortium developed a conceptual design for an Early Production System (EPS) that will serve as a mobile well test system that is safe, environmentally friendly and cost-effective. The EPS is a dynamically positioned (DP) Floating, Production, Storage and Offloading (FPSO) vessel with a bundled top tensioned riser having quick emergency disconnect capability. Both oil and gas are processed onboard and exported by shuttle tankers to local markets. Oil is stored and offloaded using standard FPSO techniques, while the gas is exported as Compressed Natural Gas (CNG). This paper summarizes the technologies, regulatory acceptance, and business model that will make the DeepStar EPS a reality. Paper published with permission.

An Artificial Intelligence Neural Networks Driven Approach to Forecast Production in Unconventional Reservoirs – Comparative Analysis with Decline Curve

2021 ◽  
Author(s):  
Hassan Khan ◽  
Clifford Louis
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Time Series ◽  
Unconventional Reservoirs ◽  
Well Test ◽  
Well Performance ◽  
Production Rates ◽  
Production Forecasting ◽  
Future Production ◽  
Decline Curve

Abstract Subsurface engineers pivot on surveillance of reservoir performance for future production rates and plan the optimization strategies at earliest. There are some techniques preferred for unconventional reservoirs such as numerical simulation and decline curve analysis (DCA) for production forecasting, but the uncertainty of uneconomical well test data often occurs in unconventional resources. Moreover, reservoir engineers can also hit a tailback in optimizing and tuning the model. Further, for DCA this approach is only appropriate for well/reservoir that are under boundary dominant flow regime, whereas fracture dominant flow regime is often observed for a longer period in unconventional hydraulically fractured reservoirs. Therefore, to resolve this issue, oil & gas industry (O&G) can adopt AI (Artificial Intelligence) based Algorithms for production forecasting. This paper presents a data-driven algorithm, known as Artificial Neural Networks (ANNs), along with time series forecasting that is a well-known statistical technique. Machine learning model trained by a past well performance data such as tubing head pressure (THP), flowing bottom-hole pressure can predict future production rates. This can be an efficient technique for subsurface engineers to monitor and optimize well performance. Time series neural networks were used for training the model at top and bottom node of the well with variating pressures in the past. After training and validation, the model predicted a target parameter that was gas rate. ANNs are inspired by biological neurons that are present in human brain, a powerful computing tool to make decisions after fueling itself with data. Moreover, prediction (t+1) nonlinear automated regression is preferred for accurate step ahead. Production rates and constraints of unconventional reservoirs were used to train the model. In our results, the NN based model gave the co-efficient of determination (R2) of 0.996 that shows nearly an exact precision. Furthermore, the values generated from NN Model and Arp's decline curve calculations were plotted for validation and it turned out that ANN can accurately predict the parameters. The Neural Network model is a novel approach for production forecasting, of unconventional reservoirs and help engineers in corporate decision making. This approach can mitigate the need of uneconomical well test operations and further provide confidence to production engineers in terms of data and result expectations.

Prediction of Liquid Loading in Gas Condensate and Volatile Oil Wells for Unconventional Reservoirs

2020 ◽  
Author(s):  
Reem Alsadoun ◽  
Mohammad Al Momen ◽  
Hongtao Luo
Keyword(s):  
Production Efficiency ◽  
Oil And Gas ◽  
Theoretical Calculations ◽  
Volatile Oil ◽  
Gas Condensate ◽  
Economic Losses ◽  
Well Performance ◽  
Liquid Loading ◽  
Critical Rate ◽  
Correlation Models

Abstract All producing wells experience reservoir pressure depletion which will ultimately cause production to cease. However, the accumulation of wellbore liquid known as liquid loading can reduce production at a faster rate bringing forward the end of well life. In theory, there are many works written on liquid loading in unconventional wells however, these assumptions are challenged when implemented in the field. The aim of this paper is to investigate the relationship between empirical and mechanistic methods used to determine liquid loading critical rates for volatile oil and gas condensate wells, improving liquid loading forecast workflow for future wells. The study was carried on a wide Pressure, Volume, and Temperature (PVT) window with varying compositions ranging from gas condensate to volatile oils. Wells with liquid loading exhibit sharp drops and fluctuations in production. Due to the wide variation in composition however, correlations used must be varied whilst accounting for both composition and horizontal configuration of the well. Using Nodal Analysis methods, Inflow Performance Relationships (IPR) and Vertical Lift Profile (VLP) curves were created from different correlation models fitted for multiple wells selected for this study to optimize well performance. By combining theoretical analysis and field practices for estimating liquid loading critical rate, the appropriate workflow was determined for the volatile oil and gas condensate wells. When comparing the critical rate for liquid loading calculated from theoretical methods against actual rates seen in the field, an inconsistency was observed between the two values for several wells. By establishing a relationship between field estimate and theoretical calculations, liquid loading was forecasted with greater certainty for varying PVT windows. When the liquid loading rate is determined earlier on, the production efficiency can be improved by deploying unloading measures, increasing the well’s producing life, and ultimately alleviating economic losses. By investigating, we were able to establish a suitable process to predict liquid loading critical rates for volatile oil and gas condensate wells. This workflow can be utilized by production engineers to arrange for liquid loading mitigation increasing well life and improving well economics.

scholarly journals Application Of Artificial Intelligence Methods In Drilling System Design And Operations: A Review Of The State Of The Art

2015 ◽  
Vol 5 (2) ◽  
pp. 121-139 ◽  
Author(s):  
Opeyemi Bello ◽  
Javier Holzmann ◽  
Tanveer Yaqoob ◽  
Catalin Teodoriu
Keyword(s):  
Artificial Intelligence ◽  
Portfolio Management ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Petroleum Exploration ◽  
Well Performance ◽  
Gas Industry ◽  
Well Production ◽  
Personnel Needs ◽  
Reservoir Simulator

AbstractArtificial Intelligence (AI) can be defined as the application of science and engineering with the intent of intelligent machine composition. It involves using tool based on intelligent behavior of humans in solving complex issues, designed in a way to make computers execute tasks that were earlier thought of human intelligence involvement. In comparison to other computational automations, AI facilitates and enables time reduction based on personnel needs and most importantly, the operational expenses.Artificial Intelligence (AI) is an area of great interest and significance in petroleum exploration and production. Over the years, it has made an impact in the industry, and the application has continued to grow within the oil and gas industry. The application in E & P industry has more than 16 years of history with first application dated 1989, for well log interpretation; drill bit diagnosis using neural networks and intelligent reservoir simulator interface. It has been propounded in solving many problems in the oil and gas industry which includes, seismic pattern recognition, reservoir characterisation, permeability and porosity prediction, prediction of PVT properties, drill bits diagnosis, estimating pressure drop in pipes and wells, optimization of well production, well performance, portfolio management and general decision making operations and many more.This paper reviews and analyzes the successful application of artificial intelligence techniques as related to one of the major aspects of the oil and gas industry, drilling capturing the level of application and trend in the industry. A summary of various papers and reports associated with artificial intelligence applications and it limitations will be highlighted. This analysis is expected to contribute to further development of this technique and also determine the neglected areas in the field.

scholarly journals The Implementation of Flow Performance Test to Monitor Well Performance in Geothermal Field

2021 ◽  
Vol 732 (1) ◽  
pp. 012020
Author(s):  
Mochamad Permadi Sugiharto ◽  
Fadiel Evan Marastio ◽  
Ahmad Fahmi Fanani ◽  
Fernando Pasaribu ◽  
Marihot Silaban ◽  
...  
Keyword(s):  
Performance Test ◽  
Geothermal Field ◽  
Well Performance

Analysis and Comparison of Seal Ability of Premium Tubing Connections under Axial Alternating Load

2013 ◽  
Vol 423-426 ◽  
pp. 2035-2039
Author(s):  
Long Cang Huang ◽  
Yin Ping Cao ◽  
Yang Yu ◽  
Yi Hua Dou
Keyword(s):  
Contact Pressure ◽  
Oil And Gas ◽  
Element Analysis ◽  
Gas Well ◽  
Cycle Number ◽  
Compression Load ◽  
Well Production ◽  
Maximum Contact ◽  
Contact Pressures ◽  
Better Than

In the process of oil and gas well production, tubing connection stand the axial alternating load during open well, shut well and fluid flow. In order to know premium connection seal ability under the loading, two types of P110 88.9mmx6.45mm premium tubing connections which called A connection and B connection are performed with finite element analysis, in which contact pressures and their the regularities distribution on sealing surface are analyzed. The results show that with the increasing of cycle number, the maximum contact pressures on sealing surface of both A connection and B connection are decreased. The decreasing of the maximum contact pressures on B connection is greater than those on A connection. With the increasing of cycle number of axial alternating compression load, the maximum contact pressure on sealing surface of A connection is decreased, and the maximum contact pressure on sealing surface of B connection remains constant. Compared the result, it shows that the seal ability of A connection is better than B connection under axial alternating tension load, while the seal ability of B connection is better than type A connection under axial alternating compression load.

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